PCA on a real pretrained weather model's latent mesh nodes reveals a dipole direction tracking synoptic wave troughs

Tempest, Beylich & Craig (2026) run PCA on 512-dimensional latent feature vectors extracted at mesh nodes after each of real pretrained GraphCast's 16 processor steps (1-degree configuration, ERA5 reanalysis input) [tempest-etal-2026-mechanistic-interpretability-tool-for-ai-weather-models] The first principal component forms a pronounced alternating dipole pattern over Northern mid-latitudes tracking synoptic-scale wave troughs (negative west / positive east of each trough); a separate leading direction correlates with specific-humidity gradients (e.g. Sahel moisture front) [tempest-etal-2026-mechanistic-interpretability-tool-for-ai-weather-models] Purely observational -- qualitative visual correspondence across two case studies and a small sample of forecast times, explicitly framed by the authors as preliminary; no causal intervention performed [tempest-etal-2026-mechanistic-interpretability-tool-for-ai-weather-models]